To tackle these limitations, we developed a 3D unified anatomy-aware cyclic adversarial network (UCAN) for translating multi-tracer PET volumes with one unified generative model, where MR with anatomical information is incorporated. Our UCAN is a paired multi-domain 3D translation model based on the previous design of unpaired 2D model of StarGAN [1]}. The general pipeline of UCAN is illustrated in Figure REF . Our UCAN uses training data of multiple tracer domains along with anatomical MR to learn the mapping between all available tracer domains using one single generator. Rather than learning a one-to-one fixed translation, our generator takes tracer volume/MR volume/tracer domain information as input, and learns to flexibly translate the tracer volume into the target tracer domain. Specifically, we use a customized label, a three-channel binary volume with one-hot-channel, for representing tracer domain information. Given trio of tracer volumes, we randomly sample a target tracer domain label and train the generator to translate an input tracer volume into the target tracer domain. During inference stage, we can simply set the tracer domain label and map the original tracer volume into the other tracer domain, hence producing multi-tracer PET volumes from single-tracer PET volume using one generator. Moreover, we proposed to use a 3D Dual Squeeze-and-Excitation Network (DuSE-Net) as our UCAN's generator to better fuse the multiple input information for synthesizing tracer volumes. Extensive experiments on human brain data demonstrate that our UCAN with DuSE-Net can efficiently produce high-quality multi-tracer PET volumes from a single-tracer PET volume using single generator.
